Endoscopic Assessment of Airway Inflammation in Horses
Bibliographic record
Abstract
BACKGROUND: Comprehensive endoscopic scoring of the upper and lower airways for inflammation has not been critically assessed among a large population of horses. The relationship between upper and lower airways described in humans by the "one airway, one disease" concept might also apply to horses. HYPOTHESIS/OBJECTIVES: To evaluate if an association exists between endoscopic inflammatory scores and mucus scores of upper and lower airways and to investigate if endoscopic findings correlate with the lower airway inflammation measured by bronchoalveolar lavage (BAL) cytology. METHODS: Prospective field study. Pharyngitis, pharyngeal mucus, tracheal mucus, tracheal septum thickness, and bronchial mucus were scored using new and previously described scoring systems on a convenience sample of 128 horses with and without lung inflammation. Based on BAL fluid cytology, horses were categorized as having normal, moderate, or severe inflammation of the lower airways. RESULTS: All 5 endoscopy scores showed excellent interobserver agreement. Tracheal mucus (P < .001), tracheal septum thickness (P = .036), and bronchial mucus (P = .037) were significantly increased in horses with severe inflammation BALs and were correlated among themselves but not with upper airways scores. BAL neutrophils percentage was correlated with tracheal mucus (r(s) = 0.41, P < .001), bronchial mucus (r(s) = 0.27, P = .003), and had a weak negative correlation with pharyngitis (r(s) = -0.25, P = .004). CONCLUSIONS AND CLINICAL IMPORTANCE: Lower airway endoscopy scores are reflective of lower airway inflammation; however, upper and lower airways are independent in terms of severity of inflammation. Therefore, observing upper airway inflammation is not an indication to test for lower airway inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".